Instance Segmentation and Localization of Strawberries in Farm Conditions for Automatic Fruit Harvesting Article Swipe
Yuanyue Ge
,
Ya Xiong
,
Pål Johan From
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1016/j.ifacol.2019.12.537
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1016/j.ifacol.2019.12.537
Accurate detection and localization of fruits is essential for strawberry harvesting robots. However, segmentation of strawberries in clusters and determination of ripeness remain challenging. Also, occlusions can result in inaccurate localization of fruits. This paper presents a method for detection, instance segmentation and better localization of strawberries, based on a deep convolutional neural network (DCNN). Four classes, including three for different ripeness levels of strawberries and one for deformed strawberries, were defined in the DCNN model. Results show that ripe strawberries are the easiest to be identified among the four classes. A bounding box refinement method was then proposed to improve the localization accuracy by detecting occluded fruits and recovering the actual fruit sizes using bounding boxes. The width to height ratio (WHR) of output masks was used to detect occlusions, and a corresponding refinement method based on the solidity of the mask shape was proposed to find the occluded side of the fruit. The refinement of occluded side is the final step, where we used the mean WHR of unoccluded strawberries to compensate the occluded part. The refinement method was assessed on the strawberry variety of 'Lusa', which shows it can estimate and recover the actual sizes. Comparison experiment shows that the bounding box overlap between the refined and ground truth is 0.87, while the overlap between raw detected and ground truth is 0.68. The result indicates that the refinement method can locate fruits more accurately.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ifacol.2019.12.537
- OA Status
- diamond
- Cited By
- 47
- References
- 18
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2998489076Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.ifacol.2019.12.537Digital Object Identifier
- Title
-
Instance Segmentation and Localization of Strawberries in Farm Conditions for Automatic Fruit HarvestingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-01-01Full publication date if available
- Authors
-
Yuanyue Ge, Ya Xiong, Pål Johan FromList of authors in order
- Landing page
-
https://doi.org/10.1016/j.ifacol.2019.12.537Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.ifacol.2019.12.537Direct OA link when available
- Concepts
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Ripeness, Bounding overwatch, Segmentation, Minimum bounding box, Artificial intelligence, Ground truth, Computer science, Convolutional neural network, Pattern recognition (psychology), Computer vision, Mathematics, Image (mathematics), Horticulture, Ripening, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
47Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 11, 2023: 12, 2022: 9, 2021: 4Per-year citation counts (last 5 years)
- References (count)
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18Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.that | 78, 201, 228 |
| abstract_inverted_index.then | 97 |
| abstract_inverted_index.used | 127, 165 |
| abstract_inverted_index.were | 70 |
| abstract_inverted_index.(WHR) | 122 |
| abstract_inverted_index.0.68. | 224 |
| abstract_inverted_index.0.87, | 213 |
| abstract_inverted_index.Also, | 24 |
| abstract_inverted_index.among | 87 |
| abstract_inverted_index.based | 47, 136 |
| abstract_inverted_index.final | 161 |
| abstract_inverted_index.fruit | 112 |
| abstract_inverted_index.masks | 125 |
| abstract_inverted_index.paper | 34 |
| abstract_inverted_index.part. | 176 |
| abstract_inverted_index.ratio | 121 |
| abstract_inverted_index.shape | 143 |
| abstract_inverted_index.shows | 189, 200 |
| abstract_inverted_index.sizes | 113 |
| abstract_inverted_index.step, | 162 |
| abstract_inverted_index.three | 58 |
| abstract_inverted_index.truth | 211, 222 |
| abstract_inverted_index.using | 114 |
| abstract_inverted_index.where | 163 |
| abstract_inverted_index.which | 188 |
| abstract_inverted_index.while | 214 |
| abstract_inverted_index.width | 118 |
| abstract_inverted_index.actual | 111, 196 |
| abstract_inverted_index.better | 43 |
| abstract_inverted_index.boxes. | 116 |
| abstract_inverted_index.detect | 129 |
| abstract_inverted_index.fruit. | 153 |
| abstract_inverted_index.fruits | 5, 107, 234 |
| abstract_inverted_index.ground | 210, 221 |
| abstract_inverted_index.height | 120 |
| abstract_inverted_index.levels | 62 |
| abstract_inverted_index.locate | 233 |
| abstract_inverted_index.method | 37, 95, 135, 179, 231 |
| abstract_inverted_index.model. | 75 |
| abstract_inverted_index.neural | 52 |
| abstract_inverted_index.output | 124 |
| abstract_inverted_index.remain | 22 |
| abstract_inverted_index.result | 27, 226 |
| abstract_inverted_index.sizes. | 197 |
| abstract_inverted_index.'Lusa', | 187 |
| abstract_inverted_index.(DCNN). | 54 |
| abstract_inverted_index.Results | 76 |
| abstract_inverted_index.between | 206, 217 |
| abstract_inverted_index.defined | 71 |
| abstract_inverted_index.easiest | 83 |
| abstract_inverted_index.fruits. | 32 |
| abstract_inverted_index.improve | 100 |
| abstract_inverted_index.network | 53 |
| abstract_inverted_index.overlap | 205, 216 |
| abstract_inverted_index.recover | 194 |
| abstract_inverted_index.refined | 208 |
| abstract_inverted_index.robots. | 11 |
| abstract_inverted_index.variety | 185 |
| abstract_inverted_index.Accurate | 0 |
| abstract_inverted_index.However, | 12 |
| abstract_inverted_index.accuracy | 103 |
| abstract_inverted_index.assessed | 181 |
| abstract_inverted_index.bounding | 92, 115, 203 |
| abstract_inverted_index.classes, | 56 |
| abstract_inverted_index.classes. | 90 |
| abstract_inverted_index.clusters | 17 |
| abstract_inverted_index.deformed | 68 |
| abstract_inverted_index.detected | 219 |
| abstract_inverted_index.estimate | 192 |
| abstract_inverted_index.instance | 40 |
| abstract_inverted_index.occluded | 106, 149, 157, 175 |
| abstract_inverted_index.presents | 35 |
| abstract_inverted_index.proposed | 98, 145 |
| abstract_inverted_index.ripeness | 21, 61 |
| abstract_inverted_index.solidity | 139 |
| abstract_inverted_index.detecting | 105 |
| abstract_inverted_index.detection | 1 |
| abstract_inverted_index.different | 60 |
| abstract_inverted_index.essential | 7 |
| abstract_inverted_index.including | 57 |
| abstract_inverted_index.indicates | 227 |
| abstract_inverted_index.Comparison | 198 |
| abstract_inverted_index.compensate | 173 |
| abstract_inverted_index.detection, | 39 |
| abstract_inverted_index.experiment | 199 |
| abstract_inverted_index.harvesting | 10 |
| abstract_inverted_index.identified | 86 |
| abstract_inverted_index.inaccurate | 29 |
| abstract_inverted_index.occlusions | 25 |
| abstract_inverted_index.recovering | 109 |
| abstract_inverted_index.refinement | 94, 134, 155, 178, 230 |
| abstract_inverted_index.strawberry | 9, 184 |
| abstract_inverted_index.unoccluded | 170 |
| abstract_inverted_index.accurately. | 236 |
| abstract_inverted_index.occlusions, | 130 |
| abstract_inverted_index.challenging. | 23 |
| abstract_inverted_index.localization | 3, 30, 44, 102 |
| abstract_inverted_index.segmentation | 13, 41 |
| abstract_inverted_index.strawberries | 15, 64, 80, 171 |
| abstract_inverted_index.convolutional | 51 |
| abstract_inverted_index.corresponding | 133 |
| abstract_inverted_index.determination | 19 |
| abstract_inverted_index.strawberries, | 46, 69 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5059695621, https://openalex.org/A5042128178, https://openalex.org/A5042074023 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I54108979 |
| citation_normalized_percentile.value | 0.9542142 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |